Overview

Dataset statistics

Number of variables12
Number of observations2599
Missing cells3928
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory243.8 KiB
Average record size in memory96.0 B

Variable types

Categorical3
Numeric9

Alerts

County FIPS Code has a high cardinality: 2599 distinct values High cardinality
County Name has a high cardinality: 1583 distinct values High cardinality
< 100% of FPL is highly correlated with ≥ 100% to ≤ 150% of FPL and 7 other fieldsHigh correlation
≥ 100% to ≤ 150% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 150% to ≤ 200% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 200% to ≤ 250% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 250% to ≤ 300% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 300% to ≤ 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Unknown is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Total Plan Selections is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
< 100% of FPL is highly correlated with ≥ 100% to ≤ 150% of FPL and 7 other fieldsHigh correlation
≥ 100% to ≤ 150% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 150% to ≤ 200% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 200% to ≤ 250% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 250% to ≤ 300% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 300% to ≤ 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Unknown is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Total Plan Selections is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
< 100% of FPL is highly correlated with ≥ 100% to ≤ 150% of FPL and 7 other fieldsHigh correlation
≥ 100% to ≤ 150% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 150% to ≤ 200% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 200% to ≤ 250% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 250% to ≤ 300% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 300% to ≤ 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Unknown is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Total Plan Selections is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
< 100% of FPL is highly correlated with ≥ 100% to ≤ 150% of FPL and 7 other fieldsHigh correlation
≥ 100% to ≤ 150% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 150% to ≤ 200% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 200% to ≤ 250% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 250% to ≤ 300% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 300% to ≤ 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
> 400% of FPL is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Unknown is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
Total Plan Selections is highly correlated with < 100% of FPL and 7 other fieldsHigh correlation
< 100% of FPL has 1228 (47.2%) missing values Missing
≥ 100% to ≤ 150% of FPL has 71 (2.7%) missing values Missing
> 150% to ≤ 200% of FPL has 59 (2.3%) missing values Missing
> 200% to ≤ 250% of FPL has 93 (3.6%) missing values Missing
> 250% to ≤ 300% of FPL has 223 (8.6%) missing values Missing
> 300% to ≤ 400% of FPL has 319 (12.3%) missing values Missing
> 400% of FPL has 1283 (49.4%) missing values Missing
Unknown has 650 (25.0%) missing values Missing
≥ 100% to ≤ 150% of FPL is highly skewed (γ1 = 23.84829317) Skewed
County FIPS Code is uniformly distributed Uniform
County FIPS Code has unique values Unique
< 100% of FPL has 134 (5.2%) zeros Zeros
> 400% of FPL has 214 (8.2%) zeros Zeros
Unknown has 49 (1.9%) zeros Zeros

Reproduction

Analysis started2022-04-06 01:15:18.812940
Analysis finished2022-04-06 01:15:33.468325
Duration14.66 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

County FIPS Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
48303
 
1
31119
 
1
48507
 
1
40079
 
1
22019
 
1
Other values (2594)
2594 

Length

Max length5
Median length5
Mean length4.928434013
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2599 ?
Unique (%)100.0%

Sample

1st row2013
2nd row2016
3rd row2020
4th row2050
5th row2060

Common Values

ValueCountFrequency (%)
483031
 
< 0.1%
311191
 
< 0.1%
485071
 
< 0.1%
400791
 
< 0.1%
220191
 
< 0.1%
471191
 
< 0.1%
481611
 
< 0.1%
311251
 
< 0.1%
311231
 
< 0.1%
560351
 
< 0.1%
Other values (2589)2589
99.6%

Length

2022-04-05T21:15:33.566818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
483031
 
< 0.1%
540271
 
< 0.1%
133211
 
< 0.1%
550891
 
< 0.1%
470291
 
< 0.1%
420251
 
< 0.1%
390971
 
< 0.1%
460931
 
< 0.1%
180671
 
< 0.1%
511711
 
< 0.1%
Other values (2589)2589
99.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

State Name
Categorical

Distinct38
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
TX
254 
GA
 
159
VA
 
134
MO
 
115
KS
 
105
Other values (33)
1832 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
TX254
 
9.8%
GA159
 
6.1%
VA134
 
5.2%
MO115
 
4.4%
KS105
 
4.0%
IL102
 
3.9%
NC100
 
3.8%
IA99
 
3.8%
TN95
 
3.7%
NE93
 
3.6%
Other values (28)1343
51.7%

Length

2022-04-05T21:15:33.670480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx254
 
9.8%
ga159
 
6.1%
va134
 
5.2%
mo115
 
4.4%
ks105
 
4.0%
il102
 
3.9%
nc100
 
3.8%
ia99
 
3.8%
tn95
 
3.7%
ne93
 
3.6%
Other values (28)1343
51.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

County Name
Categorical

HIGH CARDINALITY

Distinct1583
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size20.4 KiB
Washington County
 
22
Jackson County
 
20
Jefferson County
 
20
Franklin County
 
18
Lincoln County
 
18
Other values (1578)
2501 

Length

Max length33
Median length14
Mean length14.00230858
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1227 ?
Unique (%)47.2%

Sample

1st rowAleutians East Borough
2nd rowAleutians West Census Area
3rd rowAnchorage Municipality
4th rowBethel Census Area
5th rowBristol Bay Borough

Common Values

ValueCountFrequency (%)
Washington County22
 
0.8%
Jackson County20
 
0.8%
Jefferson County20
 
0.8%
Franklin County18
 
0.7%
Lincoln County18
 
0.7%
Union County16
 
0.6%
Clay County16
 
0.6%
Madison County16
 
0.6%
Marion County16
 
0.6%
Monroe County15
 
0.6%
Other values (1573)2422
93.2%

Length

2022-04-05T21:15:33.776116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county2464
45.8%
parish64
 
1.2%
city47
 
0.9%
st23
 
0.4%
washington23
 
0.4%
jefferson23
 
0.4%
jackson21
 
0.4%
franklin20
 
0.4%
lincoln19
 
0.4%
union17
 
0.3%
Other values (1571)2655
49.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

< 100% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct342
Distinct (%)24.9%
Missing1228
Missing (%)47.2%
Infinite0
Infinite (%)0.0%
Mean160.1167031
Minimum0
Maximum7886
Zeros134
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:33.890736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median42
Q3107.5
95-th percentile699.5
Maximum7886
Range7886
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation460.4733484
Coefficient of variation (CV)2.87586079
Kurtosis94.67786369
Mean160.1167031
Median Absolute Deviation (MAD)28
Skewness8.22466995
Sum219520
Variance212035.7046
MonotonicityNot monotonic
2022-04-05T21:15:34.016318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134
 
5.2%
2128
 
1.1%
1627
 
1.0%
1726
 
1.0%
1325
 
1.0%
1525
 
1.0%
1425
 
1.0%
1125
 
1.0%
1225
 
1.0%
1924
 
0.9%
Other values (332)1007
38.7%
(Missing)1228
47.2%
ValueCountFrequency (%)
0134
5.2%
1125
 
1.0%
1225
 
1.0%
1325
 
1.0%
1425
 
1.0%
1525
 
1.0%
1627
 
1.0%
1726
 
1.0%
1817
 
0.7%
1924
 
0.9%
ValueCountFrequency (%)
78861
< 0.1%
52361
< 0.1%
51371
< 0.1%
44311
< 0.1%
42221
< 0.1%
31941
< 0.1%
31391
< 0.1%
29581
< 0.1%
28031
< 0.1%
27921
< 0.1%

≥ 100% to ≤ 150% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1131
Distinct (%)44.7%
Missing71
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean1299.230222
Minimum0
Maximum259328
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:34.145885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q187.75
median255.5
Q3724.5
95-th percentile4167.95
Maximum259328
Range259328
Interquartile range (IQR)636.75

Descriptive statistics

Standard deviation7043.917854
Coefficient of variation (CV)5.421608686
Kurtosis769.8256506
Mean1299.230222
Median Absolute Deviation (MAD)204.5
Skewness23.84829317
Sum3284454
Variance49616778.73
MonotonicityNot monotonic
2022-04-05T21:15:34.270471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1315
 
0.6%
3014
 
0.5%
3414
 
0.5%
1413
 
0.5%
1813
 
0.5%
3513
 
0.5%
2813
 
0.5%
4013
 
0.5%
1712
 
0.5%
2212
 
0.5%
Other values (1121)2396
92.2%
(Missing)71
 
2.7%
ValueCountFrequency (%)
02
 
0.1%
118
0.3%
129
0.3%
1315
0.6%
1413
0.5%
159
0.3%
1610
0.4%
1712
0.5%
1813
0.5%
1910
0.4%
ValueCountFrequency (%)
2593281
< 0.1%
1249851
< 0.1%
944401
< 0.1%
669261
< 0.1%
598381
< 0.1%
545481
< 0.1%
445361
< 0.1%
395041
< 0.1%
389961
< 0.1%
373551
< 0.1%

> 150% to ≤ 200% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct990
Distinct (%)39.0%
Missing59
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean822.8456693
Minimum0
Maximum72468
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:34.396046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q187
median201
Q3521
95-th percentile3206.65
Maximum72468
Range72468
Interquartile range (IQR)434

Descriptive statistics

Standard deviation3005.346184
Coefficient of variation (CV)3.652381359
Kurtosis224.097408
Mean822.8456693
Median Absolute Deviation (MAD)145
Skewness12.82725677
Sum2090028
Variance9032105.684
MonotonicityNot monotonic
2022-04-05T21:15:34.639236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5020
 
0.8%
10715
 
0.6%
7414
 
0.5%
7114
 
0.5%
1413
 
0.5%
3313
 
0.5%
4813
 
0.5%
4113
 
0.5%
8412
 
0.5%
2712
 
0.5%
Other values (980)2401
92.4%
(Missing)59
 
2.3%
ValueCountFrequency (%)
03
 
0.1%
113
 
0.1%
124
 
0.2%
138
0.3%
1413
0.5%
154
 
0.2%
1610
0.4%
178
0.3%
189
0.3%
1910
0.4%
ValueCountFrequency (%)
724681
< 0.1%
496391
< 0.1%
495271
< 0.1%
453311
< 0.1%
422061
< 0.1%
287401
< 0.1%
285151
< 0.1%
217941
< 0.1%
210641
< 0.1%
208681
< 0.1%

> 200% to ≤ 250% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct818
Distinct (%)32.6%
Missing93
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean481.0794094
Minimum0
Maximum29140
Zeros6
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:34.770777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q153
median121.5
Q3325.75
95-th percentile1903.5
Maximum29140
Range29140
Interquartile range (IQR)272.75

Descriptive statistics

Standard deviation1551.151442
Coefficient of variation (CV)3.224314763
Kurtosis146.2784572
Mean481.0794094
Median Absolute Deviation (MAD)85.5
Skewness10.42576763
Sum1205585
Variance2406070.796
MonotonicityNot monotonic
2022-04-05T21:15:34.894367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3324
 
0.9%
4622
 
0.8%
3422
 
0.8%
4221
 
0.8%
7020
 
0.8%
3020
 
0.8%
3619
 
0.7%
2719
 
0.7%
5718
 
0.7%
2418
 
0.7%
Other values (808)2303
88.6%
(Missing)93
 
3.6%
ValueCountFrequency (%)
06
 
0.2%
1113
0.5%
1218
0.7%
1314
0.5%
1411
0.4%
1517
0.7%
1611
0.4%
1714
0.5%
1814
0.5%
1914
0.5%
ValueCountFrequency (%)
291401
< 0.1%
266581
< 0.1%
260741
< 0.1%
217591
< 0.1%
200431
< 0.1%
163281
< 0.1%
155071
< 0.1%
135291
< 0.1%
109681
< 0.1%
105851
< 0.1%

> 250% to ≤ 300% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct639
Distinct (%)26.9%
Missing223
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean294.8813131
Minimum0
Maximum14731
Zeros12
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:35.035112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q135
median81
Q3206.25
95-th percentile1212.5
Maximum14731
Range14731
Interquartile range (IQR)171.25

Descriptive statistics

Standard deviation839.2077704
Coefficient of variation (CV)2.845917096
Kurtosis111.1421355
Mean294.8813131
Median Absolute Deviation (MAD)56
Skewness8.90686743
Sum700638
Variance704269.6819
MonotonicityNot monotonic
2022-04-05T21:15:35.175685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2433
 
1.3%
1932
 
1.2%
2629
 
1.1%
1629
 
1.1%
1429
 
1.1%
2028
 
1.1%
2328
 
1.1%
2726
 
1.0%
3226
 
1.0%
2924
 
0.9%
Other values (629)2092
80.5%
(Missing)223
 
8.6%
ValueCountFrequency (%)
012
 
0.5%
1118
0.7%
1223
0.9%
1319
0.7%
1429
1.1%
1524
0.9%
1629
1.1%
1720
0.8%
1824
0.9%
1932
1.2%
ValueCountFrequency (%)
147311
< 0.1%
143601
< 0.1%
108101
< 0.1%
105291
< 0.1%
100901
< 0.1%
85371
< 0.1%
83181
< 0.1%
70441
< 0.1%
56101
< 0.1%
55751
< 0.1%

> 300% to ≤ 400% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct616
Distinct (%)27.0%
Missing319
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean274.3609649
Minimum0
Maximum14205
Zeros13
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:35.318815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q132
median73
Q3196.25
95-th percentile1181.95
Maximum14205
Range14205
Interquartile range (IQR)164.25

Descriptive statistics

Standard deviation740.8567103
Coefficient of variation (CV)2.700299259
Kurtosis106.9444524
Mean274.3609649
Median Absolute Deviation (MAD)51
Skewness8.443658417
Sum625543
Variance548868.6652
MonotonicityNot monotonic
2022-04-05T21:15:35.441405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1835
 
1.3%
1435
 
1.3%
2534
 
1.3%
2932
 
1.2%
2032
 
1.2%
2131
 
1.2%
1331
 
1.2%
1230
 
1.2%
3029
 
1.1%
1929
 
1.1%
Other values (606)1962
75.5%
(Missing)319
 
12.3%
ValueCountFrequency (%)
013
 
0.5%
1125
1.0%
1230
1.2%
1331
1.2%
1435
1.3%
1521
0.8%
1621
0.8%
1723
0.9%
1835
1.3%
1929
1.1%
ValueCountFrequency (%)
142051
< 0.1%
117401
< 0.1%
87221
< 0.1%
78381
< 0.1%
72501
< 0.1%
69441
< 0.1%
67071
< 0.1%
56121
< 0.1%
51081
< 0.1%
47051
< 0.1%

> 400% of FPL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct306
Distinct (%)23.3%
Missing1283
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean120.9825228
Minimum0
Maximum6068
Zeros214
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:35.575956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median29
Q384
95-th percentile555.75
Maximum6068
Range6068
Interquartile range (IQR)70

Descriptive statistics

Standard deviation342.8880701
Coefficient of variation (CV)2.834195073
Kurtosis112.3570906
Mean120.9825228
Median Absolute Deviation (MAD)26
Skewness8.786321047
Sum159213
Variance117572.2286
MonotonicityNot monotonic
2022-04-05T21:15:35.702597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0214
 
8.2%
1334
 
1.3%
1533
 
1.3%
1133
 
1.3%
1231
 
1.2%
1729
 
1.1%
1629
 
1.1%
1427
 
1.0%
1826
 
1.0%
1926
 
1.0%
Other values (296)834
32.1%
(Missing)1283
49.4%
ValueCountFrequency (%)
0214
8.2%
1133
 
1.3%
1231
 
1.2%
1334
 
1.3%
1427
 
1.0%
1533
 
1.3%
1629
 
1.1%
1729
 
1.1%
1826
 
1.0%
1926
 
1.0%
ValueCountFrequency (%)
60681
< 0.1%
46111
< 0.1%
41151
< 0.1%
27161
< 0.1%
23331
< 0.1%
22571
< 0.1%
22531
< 0.1%
19461
< 0.1%
18201
< 0.1%
15971
< 0.1%

Unknown
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct523
Distinct (%)26.8%
Missing650
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean268.5464341
Minimum0
Maximum16503
Zeros49
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:35.842130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q125
median53
Q3150
95-th percentile1168.4
Maximum16503
Range16503
Interquartile range (IQR)125

Descriptive statistics

Standard deviation904.1691085
Coefficient of variation (CV)3.366900445
Kurtosis143.5895666
Mean268.5464341
Median Absolute Deviation (MAD)36
Skewness10.16087114
Sum523397
Variance817521.7767
MonotonicityNot monotonic
2022-04-05T21:15:35.967715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
049
 
1.9%
1137
 
1.4%
1537
 
1.4%
1436
 
1.4%
1335
 
1.3%
1834
 
1.3%
1732
 
1.2%
1232
 
1.2%
1631
 
1.2%
2630
 
1.2%
Other values (513)1596
61.4%
(Missing)650
25.0%
ValueCountFrequency (%)
049
1.9%
1137
1.4%
1232
1.2%
1335
1.3%
1436
1.4%
1537
1.4%
1631
1.2%
1732
1.2%
1834
1.3%
1926
1.0%
ValueCountFrequency (%)
165031
< 0.1%
156541
< 0.1%
146021
< 0.1%
90961
< 0.1%
84581
< 0.1%
73981
< 0.1%
71111
< 0.1%
60091
< 0.1%
57481
< 0.1%
54431
< 0.1%

Total Plan Selections
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1729
Distinct (%)66.6%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3403.268387
Minimum12
Maximum392442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.4 KiB
2022-04-05T21:15:36.113224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile86.8
Q1324
median790
Q32068
95-th percentile12913.6
Maximum392442
Range392430
Interquartile range (IQR)1744

Descriptive statistics

Standard deviation13482.9973
Coefficient of variation (CV)3.96177902
Kurtosis342.4611756
Mean3403.268387
Median Absolute Deviation (MAD)586
Skewness15.37310029
Sum8838288
Variance181791216.1
MonotonicityNot monotonic
2022-04-05T21:15:36.250773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1457
 
0.3%
1007
 
0.3%
827
 
0.3%
1266
 
0.2%
626
 
0.2%
2046
 
0.2%
1645
 
0.2%
1225
 
0.2%
1635
 
0.2%
2035
 
0.2%
Other values (1719)2538
97.7%
ValueCountFrequency (%)
122
0.1%
132
0.1%
141
 
< 0.1%
153
0.1%
171
 
< 0.1%
191
 
< 0.1%
211
 
< 0.1%
221
 
< 0.1%
241
 
< 0.1%
262
0.1%
ValueCountFrequency (%)
3924421
< 0.1%
2267891
< 0.1%
2222101
< 0.1%
1572231
< 0.1%
1369681
< 0.1%
1295391
< 0.1%
1285021
< 0.1%
1085851
< 0.1%
948381
< 0.1%
940931
< 0.1%

Interactions

2022-04-05T21:15:30.903115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:19.468292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.054606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:22.608150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.079253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:25.415565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:26.803786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.327234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.609208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.080187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:19.727186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.212957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:22.794403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.229817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:25.576164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.099057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.477575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.769183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.350486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:19.930092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.359333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:22.925037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.369286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:25.726597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.238781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.616398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.899752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.492148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:20.093689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.535183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:23.114556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.521263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:25.883051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.397272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.757730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.055685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.637436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:20.246036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.693162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:23.266464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.658845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:26.027407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.559817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.895419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.199950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.787884image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:20.403451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:21.862612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:23.418489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.804350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:26.201003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.708326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.044117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.344179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:31.946817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:20.558393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T21:15:23.602596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:24.961249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:26.349371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:27.864353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.191221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.484757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T21:15:25.110188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T21:15:29.328726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.621490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-04-05T21:15:20.862731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:22.335830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:23.920716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:25.263624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:26.638654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:28.163313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:29.460289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-04-05T21:15:30.759654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-04-05T21:15:36.488138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-05T21:15:36.687152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-05T21:15:36.886566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-05T21:15:37.110490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-05T21:15:32.524583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-05T21:15:32.838726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-05T21:15:33.107165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-05T21:15:33.336908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

County FIPS CodeState NameCounty Name< 100% of FPL≥ 100% to ≤ 150% of FPL> 150% to ≤ 200% of FPL> 200% to ≤ 250% of FPL> 250% to ≤ 300% of FPL> 300% to ≤ 400% of FPL> 400% of FPLUnknownTotal Plan Selections
02013AKAleutians East BoroughNaN12.0NaNNaNNaNNaN0.0NaN29.0
12016AKAleutians West Census AreaNaNNaNNaNNaNNaNNaNNaNNaN34.0
22020AKAnchorage Municipality382.02373.01901.01620.0948.0896.0163.0465.08748.0
32050AKBethel Census Area12.012.018.020.0NaNNaNNaNNaN83.0
42060AKBristol Bay Borough0.0NaNNaN11.0NaNNaNNaN0.029.0
52068AKDenali BoroughNaN39.030.030.029.020.0NaNNaN163.0
62070AKDillingham Census AreaNaNNaN13.0NaNNaNNaNNaNNaN39.0
72090AKFairbanks North Star Borough61.0617.0494.0367.0229.0242.039.065.02114.0
82100AKHaines BoroughNaN76.050.060.027.034.0NaNNaN266.0
92105AKHoonah-Angoon Census AreaNaN44.032.016.012.0NaNNaN0.0119.0

Last rows

County FIPS CodeState NameCounty Name< 100% of FPL≥ 100% to ≤ 150% of FPL> 150% to ≤ 200% of FPL> 200% to ≤ 250% of FPL> 250% to ≤ 300% of FPL> 300% to ≤ 400% of FPL> 400% of FPLUnknownTotal Plan Selections
258956029WYPark County22.0414.0356.0290.0196.0178.016.065.01537.0
259056031WYPlatte CountyNaN64.074.052.035.051.0NaN13.0295.0
259156033WYSheridan County25.0282.0240.0204.0171.0186.013.055.01176.0
259256035WYSublette CountyNaN104.093.0105.054.0101.0NaN14.0481.0
259356037WYSweetwater County21.0307.0210.0171.0115.0126.018.050.01018.0
259456039WYTeton County26.0529.0583.0522.0403.0522.037.0100.02722.0
259556041WYUinta CountyNaN165.0130.086.059.051.0NaN22.0538.0
259656043WYWashakie CountyNaN98.077.046.021.056.0NaNNaN319.0
259756045WYWeston CountyNaN54.022.036.020.022.0NaNNaN161.0
2598XXXXXXXUnknown28.0173.0132.062.042.024.017.025.0503.0